Your browser doesn't support javascript.
loading
A Guide to Dietary Pattern-Microbiome Data Integration.
Choi, Yuni; Hoops, Susan L; Thoma, Calvin J; Johnson, Abigail J.
Afiliación
  • Choi Y; Division of Epidemiology and Community Health, University of Minnesota, School of Public Health, Minneapolis, MN.
  • Hoops SL; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, MN.
  • Thoma CJ; BioTechnology Institute, University of Minnesota, Saint Paul, MN.
  • Johnson AJ; Division of Epidemiology and Community Health, University of Minnesota, School of Public Health, Minneapolis, MN.
J Nutr ; 152(5): 1187-1199, 2022 05 05.
Article en En | MEDLINE | ID: mdl-35348723
ABSTRACT
The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet-microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern-microbiome literature focusing on dietary complexity-moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet-microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet-microbiome signals.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Microbiota / Microbioma Gastrointestinal Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Nutr Año: 2022 Tipo del documento: Article País de afiliación: Mongolia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Microbiota / Microbioma Gastrointestinal Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Nutr Año: 2022 Tipo del documento: Article País de afiliación: Mongolia
...